2018 - Fellow of the International Society for Computational Biology
His scientific interests lie mostly in Genetics, Gene, Data mining, Candidate gene and Computational biology. The Transcription factor, Transcriptional regulation, Chromosomal region and Regulator gene research he does as part of his general Gene study is frequently linked to other disciplines of science, such as Arabidopsis, therefore creating a link between diverse domains of science. His research in the fields of Enhancer overlaps with other disciplines such as Consensus sequence.
The study incorporates disciplines such as Kernel, Microarray analysis techniques, Cluster analysis, Statistical model and Gene chip analysis in addition to Data mining. His research integrates issues of Proteome, Phenotype, Proteomics, The Internet and Alzheimer's disease in his study of Candidate gene. His Computational biology study integrates concerns from other disciplines, such as Ontology, Login, Annotation, Regulation of gene expression and TBX1.
His primary areas of investigation include Genetics, Computational biology, Artificial intelligence, Data mining and Machine learning. His Gene, Genome, Comparative genomic hybridization, Human genome and DNA microarray investigations are all subjects of Genetics research. In general Gene study, his work on Gene expression and Gene expression profiling often relates to the realm of Arabidopsis, thereby connecting several areas of interest.
His Computational biology research is multidisciplinary, incorporating elements of Bioinformatics and Candidate gene. His Artificial intelligence research is multidisciplinary, relying on both Text mining and Pattern recognition. His work carried out in the field of Data mining brings together such families of science as Microarray analysis techniques and Cluster analysis.
His main research concerns Artificial intelligence, Computational biology, Machine learning, Algorithm and Drug repositioning. His biological study spans a wide range of topics, including Scalability and Pattern recognition. Yves Moreau focuses mostly in the field of Computational biology, narrowing it down to matters related to Identification and, in some cases, Genetic model and Clinical knowledge.
Yves Moreau has researched Machine learning in several fields, including Matrix decomposition, Task and Gene. His research investigates the connection between Drug repositioning and topics such as Drug target that intersect with problems in Multi-task learning, Targeted drug delivery, Genomics, Drug development and Risk analysis. Many of his studies on Disease involve topics that are commonly interrelated, such as Genetics.
Yves Moreau mainly investigates Computational biology, Gene, Artificial intelligence, Machine learning and Drug discovery. His research in Computational biology intersects with topics in High-throughput screening, Disease, Identification and High content imaging. Genetics covers Yves Moreau research in Gene.
His work on Chromosomal fragile site, Allele and Gene dosage as part of his general Genetics study is frequently connected to Pan cancer and Genome instability, thereby bridging the divide between different branches of science. His Artificial intelligence research includes elements of Simple, Theoretical computer science, Protein methods and Structural bioinformatics. His Machine learning study combines topics from a wide range of disciplines, such as Task and Matrix completion.
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PlantCARE, a database of plant cis-acting regulatory elements and a portal to tools for in silico analysis of promoter sequences
Magali Lescot;Patrice Déhais;Gert Thijs;Kathleen Marchal.
Nucleic Acids Research (2002)
BioMart and Bioconductor: a powerful link between biological databases and microarray data analysis
Steffen Durinck;Yves Moreau;Arek Kasprzyk;Sean Davis.
DECIPHER: Database of Chromosomal Imbalance and Phenotype in Humans Using Ensembl Resources
Helen V. Firth;Shola M. Richards;A. Paul Bevan;Stephen Clayton.
American Journal of Human Genetics (2009)
Gene prioritization through genomic data fusion.
Stein Aerts;Diether Lambrechts;Sunit Maity;Peter Van Loo.
Nature Biotechnology (2006)
A human phenome-interactome network of protein complexes implicated in genetic disorders
Kasper Lage;E Olof Karlberg;Zenia M Størling;Páll Í Ólason.
Nature Biotechnology (2007)
CHROMOSOME INSTABILITY IS COMMON IN HUMAN CLEAVAGE-STAGE EMBRYOS
Evelyne Vanneste;Thierry Voet;Cédric Le Caignec;Cédric Le Caignec;Michèle Ampe.
Nature Medicine (2009)
A higher-order background model improves the detection of promoter regulatory elements by Gibbs sampling.
Gert Thijs;Magali Lescot;Kathleen Marchal;Stephane Rombauts.
A Gibbs sampling method to detect overrepresented motifs in the upstream regions of coexpressed genes.
Gert Thijs;Kathleen Marchal;Magali Lescot;Stephane Rombauts.
Journal of Computational Biology (2002)
Computational tools for prioritizing candidate genes: boosting disease gene discovery
Yves Moreau;Léon-Charles Tranchevent.
Nature Reviews Genetics (2012)
Emerging patterns of cryptic chromosomal imbalance in patients with idiopathic mental retardation and multiple congenital anomalies: a new series of 140 patients and review of published reports
B. Menten;N. Maas;B. Thienpont;K. Buysse.
Journal of Medical Genetics (2006)
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